{"id":13472760,"url":"https://github.com/pykt-team/pykt-toolkit","last_synced_at":"2026-04-09T00:02:11.033Z","repository":{"id":37096382,"uuid":"492088759","full_name":"pykt-team/pykt-toolkit","owner":"pykt-team","description":"pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models","archived":false,"fork":false,"pushed_at":"2026-01-13T05:26:36.000Z","size":39890,"stargazers_count":336,"open_issues_count":58,"forks_count":100,"subscribers_count":6,"default_branch":"main","last_synced_at":"2026-01-13T08:38:12.766Z","etag":null,"topics":["deep-learning","dkt","gkt","knowledge-tracing","knowledge-tracing-models"],"latest_commit_sha":null,"homepage":"https://pykt.org","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/pykt-team.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2022-05-14T02:04:29.000Z","updated_at":"2026-01-13T05:26:41.000Z","dependencies_parsed_at":"2023-11-07T13:39:33.892Z","dependency_job_id":"a08f38a4-16aa-4b6d-b6ff-fdfe39e29e9e","html_url":"https://github.com/pykt-team/pykt-toolkit","commit_stats":null,"previous_names":[],"tags_count":5,"template":false,"template_full_name":null,"purl":"pkg:github/pykt-team/pykt-toolkit","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pykt-team%2Fpykt-toolkit","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pykt-team%2Fpykt-toolkit/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pykt-team%2Fpykt-toolkit/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pykt-team%2Fpykt-toolkit/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/pykt-team","download_url":"https://codeload.github.com/pykt-team/pykt-toolkit/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pykt-team%2Fpykt-toolkit/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31579058,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-08T14:31:17.711Z","status":"ssl_error","status_checked_at":"2026-04-08T14:31:17.202Z","response_time":54,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep-learning","dkt","gkt","knowledge-tracing","knowledge-tracing-models"],"created_at":"2024-07-31T16:00:57.756Z","updated_at":"2026-04-09T00:02:11.015Z","avatar_url":"https://github.com/pykt-team.png","language":"Python","funding_links":[],"categories":["Python"],"sub_categories":[],"readme":"# pyKT\n\n[![Downloads](https://pepy.tech/badge/pykt-toolkit)](https://pepy.tech/project/pykt-toolkit)\n[![GitHub Issues](https://img.shields.io/github/issues/pykt-team/pykt-toolkit.svg)](https://github.com/pykt-team/pykt-toolkit/issues)\n[![Documentation](https://img.shields.io/website/http/pykt-team.github.io/index.html?down_color=red\u0026down_message=offline\u0026up_message=online)](https://pykt.org/)\n\npyKT is a python library build upon PyTorch to train deep learning based knowledge tracing models. The library consists of a standardized set of integrated data preprocessing procedures on more than 7 popular datasets across different domains, 5 detailed prediction scenarios, more than 10 frequently compared DLKT approaches for transparent and extensive experiments. More details about pyKT can see our [website](https://pykt.org/) and [docs](https://pykt-toolkit.readthedocs.io/en/latest/quick_start.html).\n\n\n\n\n## Installation\nUse the following command to install pyKT:\n\nCreate conda envirment.\n\n```\nconda create --name=pykt python=3.7.5\nsource activate pykt\n```\n\n\n```\npip install -U pykt-toolkit -i  https://pypi.python.org/simple \n\n```\n\n## Hyper parameter tunning results\nThe hyper parameter tunning results of our experiments about all the DLKT models on various datasets can be found at https://drive.google.com/drive/folders/1MWYXj73Ke3zC6bm3enu1gxQQKAHb37hz?usp=drive_link.\n\n## References\n### Projects\n\n1. https://github.com/hcnoh/knowledge-tracing-collection-pytorch \n2. https://github.com/arshadshk/SAKT-pytorch \n3. https://github.com/shalini1194/SAKT \n4. https://github.com/arshadshk/SAINT-pytorch \n5. https://github.com/Shivanandmn/SAINT_plus-Knowledge-Tracing- \n6. https://github.com/arghosh/AKT \n7. https://github.com/JSLBen/Knowledge-Query-Network-for-Knowledge-Tracing \n8. https://github.com/xiaopengguo/ATKT \n9. https://github.com/jhljx/GKT \n10. https://github.com/THUwangcy/HawkesKT\n11. https://github.com/ApexEDM/iekt\n12. https://github.com/Badstu/CAKT_othermodels/blob/0c28d870c0d5cf52cc2da79225e372be47b5ea83/SKVMN/model.py\n13. https://github.com/bigdata-ustc/EduKTM\n14. https://github.com/shalini1194/RKT\n15. https://github.com/shshen-closer/DIMKT\n16. https://github.com/skewondr/FoLiBi\n17. https://github.com/yxonic/DTransformer\n18. https://github.com/lilstrawberry/ReKT\n\n### Papers\n\n1. DKT: Deep knowledge tracing \n2. DKT+: Addressing two problems in deep knowledge tracing via prediction-consistent regularization \n3. DKT-Forget: Augmenting knowledge tracing by considering forgetting behavior \n4. KQN: Knowledge query network for knowledge tracing: How knowledge interacts with skills \n5. DKVMN: Dynamic key-value memory networks for knowledge tracing \n6. ATKT: Enhancing Knowledge Tracing via Adversarial Training \n7. GKT: Graph-based knowledge tracing: modeling student proficiency using graph neural network \n8. SAKT: A self-attentive model for knowledge tracing \n9. SAINT: Towards an appropriate query, key, and value computation for knowledge tracing \n10. AKT: Context-aware attentive knowledge tracing \n11. HawkesKT: Temporal Cross-Effects in Knowledge Tracing\n12. IEKT: Tracing Knowledge State with Individual Cognition and Acquisition Estimation\n13. SKVMN: Knowledge Tracing with Sequential Key-Value Memory Networks\n14. LPKT: Learning Process-consistent Knowledge Tracing\n15. QIKT: Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representations\n16. RKT: Relation-aware Self-attention for Knowledge Tracing\n17. DIMKT: Assessing Student's Dynamic Knowledge State by Exploring the Question Difficulty Effect\n18. ATDKT: Enhancing Deep Knowledge Tracing with Auxiliary Tasks\n19. simpleKT: A Simple but Tough-to-beat Baseline for Knowledge Tracing\n20. SparseKT: Towards Robust Knowledge Tracing Models via K-sparse Attention\n21. FoLiBiKT: Forgetting-aware Linear Bias for Attentive Knowledge Tracing\n22. DTransformer: Tracing Knowledge Instead of Patterns: Stable Knowledge Tracing with Diagnostic Transformer\n23. stableKT: Enhancing Length Generalization for Attention Based Knowledge Tracing Models with Linear Biases\n24. extraKT: Extending Context Window of Attention Based Knowledge Tracing Models via Length Extrapolation\n25. csKT: Addressing Cold-start Problem in Knowledge Tracing via Kernel Bias and Cone Attention\n26. LefoKT: Rethinking and Improving Student Learning and Forgetting Processes for Attention Based Knowledge Tracing Models\n\u003c!-- 27. FlucKT: Cognitive Fluctuations Enhanced Attention Network for Knowledge Tracing --\u003e\n27. UKT: Uncertainty-aware Knowledge Tracing\n28. HCGKT: Hierarchical Contrastive Graph Knowledge Tracing with Multi-level Feature Learning\n29. RobustKT: Enhancing Knowledge Tracing through Decoupling Cognitive Pattern from Error-Prone Data\n\n## Citation\n\nWe now have a [paper](https://arxiv.org/abs/2206.11460?context=cs.CY) you can cite for the our pyKT library:\n\n```bibtex\n@inproceedings{liupykt2022,\n  title={pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models},\n  author={Liu, Zitao and Liu, Qiongqiong and Chen, Jiahao and Huang, Shuyan and Tang, Jiliang and Luo, Weiqi},\n  booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},\n  year={2022}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpykt-team%2Fpykt-toolkit","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpykt-team%2Fpykt-toolkit","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpykt-team%2Fpykt-toolkit/lists"}